In identification processing using machine learning, there is a method called supervised learning in which a set of input data and a correct answer label corresponding to the input data is prepared as learning data to thereby update a parameter. For the learning data, it is effective to exhaustively use data which can be actually input when the identification processing is executed. In order to cover the data which can be input when the identification processing is executed, it is effective to prepare a large amount of the learning data, or to use, for the identification processing, data acquired in an environment similar to an environment where recognition processing is performed. However, correct answer data for use in the identification processing are manually assigned in general, and accordingly, there is a problem that human cost is increased when an amount of the data is increased.
PTL 1 describes a learning data generation system that generates learning data by extracting an object region, which is a region on which an object is captured, from respective object-captured images captured while continuously changing imaging conditions such as information regarding a position of a camera with respect to such a subject.
Moreover, PTL 2 describes an active learning system that, with regard to data in which a value of a label is unknown, calculates a similarity thereof to data in which a value of a label is a predetermined value, and selects data to be learned next on the basis of the calculated similarity.
Furthermore, PTL 3 describes one example of a technique for detecting and tracking a position of a person by using measurement data of a laser range sensor.